@article{wang2018deepigeos,
  title     = {DeepIGeoS: a deep interactive geodesic framework for medical image segmentation},
  author    = {Wang, Guotai and Zuluaga, Maria A and Li, Wenqi and Pratt, Rosalind and Patel, Premal A and Aertsen, Michael and Doel, Tom and David, Anna L and Deprest, Jan and Ourselin, S{\'e}bastien and others},
  journal   = {IEEE transactions on pattern analysis and machine intelligence},
  volume    = {41},
  number    = {7},
  pages     = {1559--1572},
  year      = {2018},
  publisher = {IEEE}
}

@inproceedings{criminisi2008geos,
  title        = {Geos: Geodesic image segmentation},
  author       = {Criminisi, Antonio and Sharp, Toby and Blake, Andrew},
  booktitle    = {European Conference on Computer Vision},
  pages        = {99--112},
  year         = {2008},
  organization = {Springer},
  doi          = {10.1007/978-3-540-88682-2_9}
}

@article{weber2008parallel,
  title     = {Parallel algorithms for approximation of distance maps on parametric surfaces},
  author    = {Weber, Ofir and Devir, Yohai S and Bronstein, Alexander M and Bronstein, Michael M and Kimmel, Ron},
  journal   = {ACM Transactions on Graphics (TOG)},
  volume    = {27},
  number    = {4},
  pages     = {1--16},
  year      = {2008},
  publisher = {ACM New York, NY, USA},
  doi       = {10.1145/1409625.1409626}
}

@misc{geodistk,
  author    = {Wang, Guotai},
  title     = {GeodisTK: Geodesic Distance Transform Toolkit for 2D and 3D Images},
  year      = {2020},
  publisher = {GitHub},
  journal   = {GitHub repository},
  url       = {https://github.com/taigw/GeodisTK}
}

@misc{eucildeantdimpl,
  author    = {Seung-Lab, },
  title     = {Multi-Label Anisotropic 3D {E}uclidean Distance Transform (MLAEDT-3D)},
  year      = {2018},
  publisher = {GitHub},
  journal   = {GitHub repository},
  url       = {https://github.com/seung-lab/euclidean-distance-transform-3d}
}


@article{asad2022econet,
  title   = {ECONet: Efficient Convolutional Online Likelihood Network for Scribble-based Interactive Segmentation},
  author  = {Asad, Muhammad and Fidon, Lucas and Vercauteren, Tom},
  journal = {arXiv preprint arXiv:2201.04584},
  year    = {2022}
}

@incollection{NEURIPS2019_9015,
  title     = {PyTorch: An Imperative Style, High-Performance Deep Learning Library},
  author    = {Paszke, Adam and Gross, Sam and Massa, Francisco and Lerer, Adam and Bradbury, James and Chanan, Gregory and Killeen, Trevor and Lin, Zeming and Gimelshein, Natalia and Antiga, Luca and Desmaison, Alban and Kopf, Andreas and Yang, Edward and DeVito, Zachary and Raison, Martin and Tejani, Alykhan and Chilamkurthy, Sasank and Steiner, Benoit and Fang, Lu and Bai, Junjie and Chintala, Soumith},
  booktitle = {Advances in Neural Information Processing Systems 32},
  editor    = {H. Wallach and H. Larochelle and A. Beygelzimer and F. d\textquotesingle Alch\'{e}-Buc and E. Fox and R. Garnett},
  pages     = {8024--8035},
  year      = {2019},
  publisher = {Curran Associates, Inc.},
  url       = {http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf}
}

@inproceedings{criminisiinteractive,
  author    = {Criminisi, Antonio and Sharp, Toby and Siddiqui, Khan},
  title     = {Interactive {G}eodesic Segmentation of n-Dimensional Medical Images on the Graphics Processor},
  booktitle = {Radiological Society of North America (RSNA)},
  year      = {2009},
  month     = {December},
  abstract  = {This paper presents a new, parallel segmentation algorithm which enables radiologists to separate a region of interest from 2D or 3D images accurately and efficiently. This, in turn, enables fast and accurate area/volume measurements as well as the extraction of statistics for the selected region. Our general purpose algorithm can be applied to any visible structure (e.g. a tumor or any structure) and it is driven by minimal and intuitive user interaction.},
  url       = {https://www.microsoft.com/en-us/research/publication/interactive-geodesic-segmentation-of-n-dimensional-medical-images-on-the-graphics-processor/},
  edition   = {Radiological Society of North America (RSNA)}
}

@article{felzenszwalb2012distance,
  title     = {Distance transforms of sampled functions},
  author    = {Felzenszwalb, Pedro F and Huttenlocher, Daniel P},
  journal   = {Theory of computing},
  volume    = {8},
  number    = {1},
  pages     = {415--428},
  year      = {2012},
  publisher = {Theory of Computing Exchange}
}

@article{toivanen1996new,
  title     = {New geodosic distance transforms for gray-scale images},
  author    = {Toivanen, Pekka J},
  journal   = {Pattern Recognition Letters},
  volume    = {17},
  number    = {5},
  pages     = {437--450},
  year      = {1996},
  publisher = {Elsevier},
  doi       = {10.1016/0167-8655(96)00010-4}
}

@misc{tensorflow2015-whitepaper,
  title  = { {TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
  url    = {https://www.tensorflow.org/},
  note   = {Software available from tensorflow.org},
  author = {
            Mart\'{i}n~Abadi and
            Ashish~Agarwal and
            Paul~Barham and
            Eugene~Brevdo and
            Zhifeng~Chen and
            Craig~Citro and
            Greg~S.~Corrado and
            Andy~Davis and
            Jeffrey~Dean and
            Matthieu~Devin and
            Sanjay~Ghemawat and
            Ian~Goodfellow and
            Andrew~Harp and
            Geoffrey~Irving and
            Michael~Isard and
            Yangqing Jia and
            Rafal~Jozefowicz and
            Lukasz~Kaiser and
            Manjunath~Kudlur and
            Josh~Levenberg and
            Dandelion~Man\'{e} and
            Rajat~Monga and
            Sherry~Moore and
            Derek~Murray and
            Chris~Olah and
            Mike~Schuster and
            Jonathon~Shlens and
            Benoit~Steiner and
            Ilya~Sutskever and
            Kunal~Talwar and
            Paul~Tucker and
            Vincent~Vanhoucke and
            Vijay~Vasudevan and
            Fernanda~Vi\'{e}gas and
            Oriol~Vinyals and
            Pete~Warden and
            Martin~Wattenberg and
            Martin~Wicke and
            Yuan~Yu and
            Xiaoqiang~Zheng},
  year   = {2015}
}

@misc{bronstein2013parallel,
  title={Parallel approximation of distance maps (US Patent 8,373,716)},
  author={Bronstein, Alexander and Bronstein, Michael and Devir, Yohai and Weber, Ofir and Kimmel, Ron},
  year={2013},
  month=feb # "~12",
  publisher={Google Patents},
  note={US Patent 8,373,716}
}

@misc{bronstein2015parallel,
  title={Parallel approximation of distance maps (US Patent 8,982,142)},
  author={Bronstein, Alexander and Bronstein, Michael and Kimmel, Ron and Devir, Yohai and Weber, Ofir},
  year={2015},
  month=mar # "~17",
  publisher={Google Patents},
  note={US Patent 8,982,142}
}

@misc{bronstein2016parallel,
  title={Parallel approximation of distance maps (US Patent 9,489,708)},
  author={Bronstein, Alexander and Bronstein, Michael and Kimmel, Ron and Devir, Yohai and Weber, Ofir},
  year={2016},
  month=nov # "~8",
  publisher={Google Patents},
  note={US Patent 9,489,708}
}
